How can we use the 'log-likelihood', coming out from Hidden Markov model (HMM) predictor, to define the conditional probability tables of a Bayesian network (BN) in a HMM-BN hybrid system?

Usually these are used for events with several orders of magnitude variation in both probability and magnitudeFor example, one tree burning is a very common event. Lighting strikes cause this often. Burning an entire forest with millions of trees is rare, but may actually kill more trees, in spite of rarity.There are a lot of these kinds of distributions in risk analysis. Earthquake distributions also look like this.You can use a log table, log normal distributions or a metalog distribution.

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How can we use the 'log-likelihood', coming out from Hidden Markov model (HMM) predictor, to define the conditional probability tables of a Bayesian network (BN) in a HMM-BN hybrid system?
Usually these are used for events with several orders of magnitude variation in both probability and magnitudeFor example, one tree burning is a very common event. Lighting strikes cause this often. Burning an entire forest with millions of trees is rare, but may actually kill more trees, in spite of rarity.There are a lot of these kinds of distributions in risk analysis. Earthquake distributions also look like this.You can use a log table, log normal distributions or a metalog distribution.

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